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Título: Sample size determination for prevalence estimation in the absence of a gold standard diagnostic test
Autores: Rahme, Elham H.
Fecha: 1996
Publicador: McGill University - MCGILL
Fuente:
Tipo: Electronic Thesis or Dissertation
Tema: Biology, Biostatistics.
Descripción: A common problem in medical research is the estimation of the prevalence of a disease in a given population. This is usually accomplished by applying a diagnostic test to a sample of subjects from the target population. In this thesis, we investigate the sample size requirements for the accurate estimation of disease prevalence for such experiments. When a gold standard diagnostic test is available, estimating the prevalence of a disease can be viewed as a problem in estimating a binomial proportion. In this case, we discuss some anomalies in the classical sample size criteria for binomial parameter estimation. These are especially important with small sample sizes. When a gold standard test is not available, one must take into account misclassification errors in order to avoid misleading results. When the sensitivity and the specificity of the diagnostic test are both known, a new adjustment to the maximum likelihood estimator of the prevalence is suggested, and confidence intervals and sample size estimates that arise from this estimator are given. A Bayesian approach is taken when the sensitivity and specificity of the diagnostic test are not exactly known. Here, a method to determine the sample size needed to satisfy a Bayesian sample size criterion that averages over the preposterior marginal distribution of the data is provided. Exact methods are given in some cases, and a sampling importance resampling algorithm is used for more complex situations. A main conclusion is that the degree to which the properties of a diagnostic test are known can have a very large effect on the sample size requirements.
Idioma: en